{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:13Z","timestamp":1723016053967},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks. However, previous MANNs suffer from complex memory addressing mechanism, making them relatively hard to train and causing computational overheads. Moreover, many of them reuse the classical RNN structure such as LSTM for memory processing, causing inefficient exploitations of memory information. In this paper, we introduce a novel MANN, the Auto-addressing and Recurrent Memory Integrating Network (ARMIN) to address these issues. The ARMIN only utilizes hidden state h_t for automatic memory addressing, and uses a novel RNN cell for refined integration of memory information. Empirical results on a variety of experiments demonstrate that the ARMIN is more light-weight and efficient compared to existing memory networks. Moreover, we demonstrate that the ARMIN can achieve much lower computational overhead than vanilla LSTM while keeping similar performances. Codes are available on github.com\/zoharli\/armin.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/408","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"2944-2951","source":"Crossref","is-referenced-by-count":2,"title":["ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network"],"prefix":"10.24963","author":[{"given":"Zhangheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University"},{"name":"Peng Cheng Laboratory"}]},{"given":"Jia-Xing","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University"},{"name":"Peng Cheng Laboratory"}]},{"given":"Jingjia","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University"},{"name":"Peng Cheng Laboratory"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University"},{"name":"Peng Cheng Laboratory"}]},{"given":"Thomas","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University"}]},{"given":"Ge","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University"},{"name":"Peng Cheng Laboratory"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:49:05Z","timestamp":1564285745000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/408"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/408","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}